19 papers · ranked by Valyu relevance
Florian Obermüller, Lena Bloch, Luisa Greifenstein, Ute Heuer + 1 more
'Gordon Fraser'] Block-based programming languages like Scratch enable children to be creative while learning to program. Even though the blockbased approach simplifies the creation of programs, learning to program can nevertheless be challenging. Automated tools such as linters therefore support learners by providing…
Xiaofeng Han, Amjed Tahir, Peng Liang, Steve Counsell + 3 more
'Kelly Blincoe' 'Bing Li' 'Yajing Luo'] Abstract Code review plays an important role in software quality control. A typical review process involves a careful check of a piece of code in an attempt to detect and locate defects and other quality issues/violations. One type of issue that may impact the quality of software…
Debalina Ghosh Paul, Hong Zhu, Ian Bayley
The method measures code smells, an important indicator of code quality, and compares them with a baseline formed from reference solutions of professionally written code. The test dataset is divided into various subsets according to the topics of the code and complexity of the coding tasks to represent different…
Nawaf Alomari, Amal Alazba, Hamoud Aljamaan, Mohammad Alshayeb
Context: Code smells indicate poor software design, affecting maintainability. Accurate detection is vital for refactoring and quality improvement. However, existing datasets often frame detection as single-label classification, limiting realism. Objective: This paper develops a multi-label dataset for code smell…
Fabiano Pecorelli, Savanna Lujan, Valentina Lenarduzzi, Fabio Palomba + 1 more
Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine…
Riasat Mahbub, Mohammad Masudur Rahman, Muhammad Ahsanul Habib
Modelling Software Authors: ['Riasat Mahbub' 'Mohammad Masudur Rahman' 'Muhammad Ahsanul Habib'] Abstract—Simulation modelling systems are routinely used to test or understand real-world scenarios in a controlled setting. They have found numerous applications in scientific research, engineering, and industrial…
Zhipeng Xue, Xiaoting Zhang, Zhipeng Gao, Xing Hu + 3 more
The Large Language Models (LLMs) have demonstrated great potential in code-related tasks. However, most research focuses on improving the output quality of LLMs (e.g., correctness), and less attention has been paid to the LLM input (e.g., the training code quality). Given that code smells are widely existed in practice…
Rana Sandouka, Hamoud Aljamaan, Stephen Piccolo
Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code…
Ruchin Gupta, Sandeep Kumar Singh
Code smells are indicators of potential design flaws in source code and do not appear alone but in combination with other smells, creating complex interactions. While existing literature classifies these smell interactions into collocated, coupled, and inter-smell relations, however, to the best of our knowledge, no…
Aakanshi Gupta, Bharti Suri, Deepanshu Sharma, Sanjay Misra + 1 more
'Luis Fernandez-Sanz'] In the digitization era, the battery consumption factor plays a vital role for the devices that operate Android software, expecting them to deliver high performance and good maintainability.The study aims to analyze the Android-specific code smells, their impact on battery consumption, and the…
Hamoud Aljamaan, Muhammad Aleem
Code smells refer to poor design and implementation choices by software engineers that might affect the overall software quality. Code smells detection using machine learning models has become a popular area to build effective models that are capable of detecting different code smells in multiple programming languages.…
Anh Ho, Anh M. T. Bui, Phuong T. Nguyen, Amleto Di Salle + 1 more
code smells detection Authors: ['Anh Ho' 'Anh M. T. Bui' 'Phuong T. Nguyen' 'Amleto Di Salle' 'Bach Le'] A smell in software source code denotes an indication of suboptimal design and implementation decisions, potentially hindering the code understanding and, in turn, raising the likelihood of being prone to changes…
Valeria Pontillo, Dario Amoroso d’Aragona, Fabiano Pecorelli, Dario Di Nucci + 2 more
Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these…
Seyone Chithrananda, Judith Amores, Kevin K. Yang
The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules activate a subset of our olfactory receptors and combinations of unique receptor activations code for unique odors.…
Jessica L. Zung, Sumer M. Kotb, Carolyn S. McBride
The natural world is full of odours—blends of volatile chemicals emitted by potential sources of food, social partners, predators, and pathogens. Animals rely heavily on these signals for survival and reproduction. Yet we remain remarkably ignorant of the composition of the chemical world. How many compounds do natural…
Jason B. Castro, Travis J. Gould, Robert Pellegrino, Zhiwei Liang + 6 more
Advances in theoretical understanding are frequently unlocked by access to large, diverse experimental datasets. Olfactory neuroscience and psychophysics remain years behind the other senses in part because rich datasets linking olfactory stimuli with their corresponding percepts, behaviors, and neural pathways…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
The mechanisms underlying human odor recognition remain largely unclear, making it challenging to predict the scent of a novel molecule based solely on its molecular structure. Unlike taste, which is classified into a limited number of categories, odor perception is highly complex and lacks universally defined labels…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…